{
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   "metadata": {
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   "source": [
    "# ML_in_Finance-RNNs-HFT\n",
    "# Author: Matthew Dixon\n",
    "# Version: 1.1 (27.2.2020)\n",
    "# License: MIT\n",
    "# Email: matthew.dixon@iit.edu\n",
    "# Notes: tested on Mac OS X running Python 3.6.9 with the following packages:\n",
    "# tensorflow=2.0.0, keras=2.3.1, scikit-learn=0.22.1, numpy=1.18.1, matplotlib=3.1.3, pandas=1.0.3, statsmodels=0.10.1\n",
    "# Citation: Please cite the following reference if this notebook is used for research purposes:\n",
    "# Bilokon P., Dixon M.F. and Halperin I., Machine Learning in Finance: From Theory to Practice, Springer Graduate Textbook Series, 2020. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OQ-9CiFdPaHU"
   },
   "source": [
    "## An Introduction to Prediction with RNNs\n",
    "\n",
    "### Overview\n",
    "- This notebook provides an example of how Keras can be used to train and test TensorFlow RNNs for time series prediction. The example dataset is for predicting from noisy, non-stationary data.\n",
    "- Statistical methods used for autoregressive models shall be used to identify the sequence length needed in the RNN and to diagnose the model error.\n",
    "- Plain RNNs are not suited to non-stationary time series modeling. We can use a GRU or LSTM to model non-stationary data, since these models exhibit dynamic auto-correlation structure.\n",
    "- Unlike classical time series methods, e.g. ARIMA, there are no parametric assumptions on the distribution of the errors, and non-linear relationships between response and predictors can be captured. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "d86mQwhTPaHX"
   },
   "source": [
    "#### Statistician's note\n",
    "- We choose to build a model which provides strong predictive power, at the expense of reduced explanatory power. \n",
    "- Our choice to use a recurrent neural network is predicated on each observation in the time series being dependent on previous observations. The ordering of the observations therefore matters and $X$ is not iid.\n",
    "- Once the input data is appropriately scaled, model building starts with 'feature selection' - identifying the relevant features to include in the model. \n",
    "\n",
    "- In this notebook, we assume that we've already identifed the relevant set of features (i.e. there is only one time series provided).\n",
    "- Our primary concern is assessing the extent to which the model is over-fitting, by comparing the in- and out-of-sample MSEs.\n",
    "\n",
    "#### Implementation notes\n",
    "- It is important to ensure that `shuffle=False` in the fit function, otherwise the ordering of sequences is not preserved. This is especially important for methods which have memory beyond the current sequence (i.e. all methods except RNNs).\n",
    "- Time series cross-validation must be used for hyper-parameter tuning because the ordering of the data matters. In particular, the model must never use training data more recent than the forecasting observation date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "id": "Yqcjt5vRPaHc",
    "outputId": "b0a27dfc-cbf0-4067-9c27-9ba5af834f33",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dez/opt/miniconda3/envs/MLFenv/lib/python3.6/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import tensorflow as tf\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold, TimeSeriesSplit, GridSearchCV\n",
    "\n",
    "import keras.initializers\n",
    "from keras.layers import Dense, Layer, LSTM, GRU, SimpleRNN, RNN\n",
    "from keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "from keras.regularizers import l1, l2\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "\n",
    "# note that the directory containing these two .py's must be in the path variable:\n",
    "from alphaRNN import AlphaRNN\n",
    "from alphatRNN import AlphatRNN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4cMaFTcxPaHq"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rCbaqIg4PaH3"
   },
   "source": [
    "### Example Data\n",
    "- The example dataset is a chronologically ordered time series. The ordering of the observations matters and each observation is not assumed to be independent (as with cross-sectional classification data). \n",
    "\n",
    "- For simplicity, since the timestamps are not important for this tutorial, an integer index has been used for the Pandas Dataframe. A real dataset would typically be indexed by a unique timestamp. The observations are not uniform and in general are less than a micro-second apart.\n",
    "\n",
    "- Each observation $X$ has three variables (a.k.a. features). Feature 3 is the smart price (VWAP) and the label indicates whether the book will up-tick, stay flat or down-tick in the next time interval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bJ_66DyTPaH4"
   },
   "source": [
    "Loading the Pandas Dataframe, viewing the first ten rows and the distribution of the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-U_9vR1xPaH6"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/HFT.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "colab_type": "code",
    "id": "SC8j_b46PaIE",
    "outputId": "0be4a023-c195-494d-eb1f-d3a4226352fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_1</th>\n",
       "      <th>feature_2</th>\n",
       "      <th>feature_3</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
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       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.515301</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.710953</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   feature_1  feature_2  feature_3  label\n",
       "0   0.515301       0.72   0.710953      0\n",
       "1   0.515301       0.72   0.710953      0\n",
       "2   0.515301       0.72   0.710953      0\n",
       "3   0.515301       0.72   0.710953      0\n",
       "4   0.515301       0.72   0.710953      0\n",
       "5   0.515301       0.72   0.710953      0\n",
       "6   0.515301       0.72   0.710953      0\n",
       "7   0.515301       0.72   0.710953      0\n",
       "8   0.515301       0.72   0.710953      0\n",
       "9   0.515301       0.72   0.710953      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eWJWjItJPaIN"
   },
   "source": [
    "# RNN Regression\n",
    "We consider a univariate prediction problem where the time series is given by 'feature_3' in the data frame, and for each input sequence we predict the value 10 time-steps into the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-SBLK3YqPaIO"
   },
   "outputs": [],
   "source": [
    "use_features = ['feature_3'] # continuous input\n",
    "target = ['feature_3'] # continuous output\n",
    "n_steps_ahead = 10 # forecasting horizon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NM2G9x6sPaIW"
   },
   "source": [
    "### Stationarity\n",
    "It is essential to determine whether the time series is \"stationary\". Informally, stationarity is when the auto-covariance is independent of time. Failure to establish stationarity will almost certainly lead to misinterpretation of model identification and diagnostics tests. Moreover, stationarity is decisive in characterizing the prediction problem and whether to use a more advanced architecture. In particular, we can expect a plain RNN to perform poorly if the data is non-stationary as the RNN exhibits fixed auto-covariance. \n",
    "\n",
    "We perform an Augmented Dickey-Fuller test to establish stationarity under the assumption that the time series has a constant bias but does not exhibit a time trend. In other words, we assume that the time series is already de-trended. \n",
    "\n",
    "If the stationarity test fails, even after first de-trending the time series, then one potential recourse is to simply take differences of time series and predict $\\Delta y_t$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P3W7nYDPPaIY"
   },
   "source": [
    "The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the p-value is above $(1-\\alpha)$, then we cannot reject that there is a unit root. Note that a subset of the time series is used to reduce the computation time of the test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OcFNxkZSPaIZ"
   },
   "outputs": [],
   "source": [
    "sample = df['feature_3'][:200000]\n",
    "adf, p, usedlag, nobs, cvs, aic = sm.tsa.stattools.adfuller(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86
    },
    "colab_type": "code",
    "id": "sylN6LyiPaIe",
    "outputId": "a06dd49f-5566-4998-ad90-4460ddf66efe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF: -1.2215995010265421\n",
      "p-value: 0.6642358271469121,\n",
      "N: 1995, \n",
      "critical values: {'1%': -3.4336320721769433, '5%': -2.862989840784964, '10%': -2.56754183359401}\n"
     ]
    }
   ],
   "source": [
    "adf_results_string = 'ADF: {}\\np-value: {},\\nN: {}, \\ncritical values: {}'\n",
    "print(adf_results_string.format(adf, p, nobs, cvs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Oj094O4PaIp"
   },
   "source": [
    "#### Autoregressive Model Identification: The partial auto-correlation\n",
    "It is important to determine the number of lags, the sequence length, required in the RNN by statistical analysis. A brute-force approach will in general be too time-consuming.\n",
    "\n",
    "A partial auto-correlation at lag $h\\geq 2$ is a conditional auto-correlation between a variable, $X_t$, and its $h^{th}$ lag, $X_{t-h}$ under the assumption that we control for the values of the intermediate lags, $X_{t-1},\\dots, X_{t-h+1}$:\n",
    "\n",
    "$$\\begin{align}\\tau_h&:=\\tau(X_t, X_{t-h}; X_{t-1},\\dots, X_{t-h+1})\\\\\n",
    "&:=\\frac{\\gamma(X_t, X_{t-h}; X_{t-1},\\dots, X_{t-h+1})}{\\sqrt{\\gamma(X_t |X_{t-1},\\dots, X_{t-h+1})\\gamma(X_{t-h} |X_{t-1},\\dots, X_{t-h+1}))}}\n",
    ",\\end{align}$$\n",
    "where $\\gamma_h:=\\gamma(X_tX_{t-h})$ is the lag-$h$ autocovariance. The partial autocorrelation function $\\tau_h:\\mathbb{N} \\rightarrow [-1,1]$ is a map $h:\\mapsto \\tau_h$.\n",
    "\n",
    "The estimated partial auto-correlation function (PACF) can be used to identify the order of an autoregressive time series model. Values of $|\\tau_h|$ greater or equal to $\\frac{\\Phi^{-1}(\\alpha)}{\\sqrt{T}}$, where $T$ is the number of observations and $\\Phi(z)$ is the standard normal CDF, are significant lag $h$ partial autocorelations at the $\\alpha$ confidence level.\n",
    "\n",
    "We use the stattools package to estimat the PACF. The `nlags` parameter is the maximum number of lags used for PACF estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5VCmpmfePaIq"
   },
   "outputs": [],
   "source": [
    "pacf = sm.tsa.stattools.pacf(df[use_features], nlags=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kSB6x8_PaI2"
   },
   "source": [
    "Since $\\Phi^{-1}(0.99) \\simeq 2.58$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xzsy_bjyqjwh"
   },
   "outputs": [],
   "source": [
    "T = len(df[use_features])\n",
    "\n",
    "sig_test = lambda tau_h: np.abs(tau_h) > 2.58/np.sqrt(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vICFGFhXPaI9"
   },
   "source": [
    "We find the first lag which isn't significant at the 99% level and automatically determine the number of lags needed in our autoregressive model as one below this value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WPUaneVAPaI_",
    "outputId": "23baf06b-57a1-41ac-984f-2b16d9e26119"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_steps set to 23\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(pacf)):\n",
    "    if sig_test(pacf[i]) == False:\n",
    "        n_steps = i - 1\n",
    "        print('n_steps set to', n_steps)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mjH5GeLzPaJK"
   },
   "source": [
    "This may lead to a high order model, with more lags than strictly necessary. We could view this value, informally, as an upper bound on the number of lags needed. We can also simply identify the order of the model based on the plot of the PACF. In this case, a minimum of 2 lags appears satisfactory, although more may be needed. Unlike autoregressive models, the advantage of using fewer parameters is purely computational as adding more lags does not increase the number of parameters, only the size of the tensorial representation of the sequence data in TensorFlow. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "u7HwDt10PaJL",
    "outputId": "2e592f0f-7baa-4893-fe3b-21bb8fbac82f",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pacf, label='pacf')\n",
    "plt.plot([2.58/np.sqrt(T)]*30, label='99% confidence interval (upper)')\n",
    "plt.plot([-2.58/np.sqrt(T)]*30, label='99% confidence interval (lower)')\n",
    "plt.xlabel('number of lags')\n",
    "plt.xticks(np.arange(0, 30, 2))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VujfyquLPaJQ"
   },
   "source": [
    "### Splitting the time series into training and testing sets\n",
    "Split the training and test set by using the first 80% of the time series and the remaining 20% for the test set. Note that the test set must be in the future of the training set to avoid look-ahead bias. Also, random sampling of the data can not be used as this would eliminate the auto-correlation structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JboSM1q4PaJS"
   },
   "outputs": [],
   "source": [
    "train_weight = 0.8\n",
    "split = int(len(df)*train_weight)\n",
    "\n",
    "df_train = df[use_features].iloc[:split]\n",
    "df_test = df[use_features].iloc[split:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "GZWNtNVGPaJa"
   },
   "source": [
    "### Scaling\n",
    "Standardization of the data is important to avoid potential scaling difficulties in the fitting of the model. When there is more than one feature (covariate), scaling avoids one feature dominating over another due to disparate scales.\n",
    "\n",
    "To avoid introducing a look-ahead bias into the prediction, we must re-scale the training data without knowledge of the test set. Hence, we will simply standardize the training set using the mean and standard deviation of the training set and not the whole time series. Additionally, to avoid introducing a systematic bias into test set, we use the identical normalization for the test set - the mean and standard deviation of the training set are used to normalize the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "boEtEpSEPaJb"
   },
   "outputs": [],
   "source": [
    "# note that for a multivariate time series, you would need to scale \n",
    "# each variable by its own mean and standard deviation in the training set\n",
    "mu = np.float(df_train.mean())\n",
    "sigma = np.float(df_train.std())\n",
    "\n",
    "stdize_input = lambda x: (x - mu) / sigma\n",
    "\n",
    "df_train = df_train.apply(stdize_input)\n",
    "df_test = df_test.apply(stdize_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y80JdabFPaJg"
   },
   "source": [
    "### Data formatting for RNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vMUWFJsrPaJj"
   },
   "source": [
    "TensorFlow uses tensors to represent data. To perform sequence learning, the time series of variables must be transformed to a series of over-lapping sub-sequences. \n",
    "\n",
    "For example, consider a univariate time series of increasing integers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "deh_8Tk4PaJk"
   },
   "source": [
    "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-RmjOgIZPaJm"
   },
   "source": [
    "Setting the sequence length to 10, for example, we move the window forward by one observation at a time and construct new sequences:\n",
    "\n",
    "1 2 3 4 5 6 7 8 9 10\n",
    "\n",
    "2 3 4 5 6 7 8 9 10 11\n",
    "\n",
    "3 4 5 6 7 8 9 10 11 12\n",
    "\n",
    "4 5 6 7 8 9 10 11 12 13\n",
    "\n",
    "5 6 7 8 9 10 11 12 13 14\n",
    "\n",
    "6 7 8 9 10 11 12 13 14 15\n",
    "\n",
    "\n",
    "Let's define the following function for reshaping the data into one-step ahead times series prediction format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Mf9InhYAPaJn"
   },
   "outputs": [],
   "source": [
    "def get_lagged_features(df, n_steps, n_steps_ahead):\n",
    "    \"\"\"\n",
    "    df: pandas DataFrame of time series to be lagged\n",
    "    n_steps: number of lags, i.e. sequence length\n",
    "    n_steps_ahead: forecasting horizon\n",
    "    \"\"\"\n",
    "    lag_list = []\n",
    "    for lag in range(n_steps + n_steps_ahead - 1, n_steps_ahead - 1, -1):\n",
    "        lag_list.append(df.shift(lag))\n",
    "    lag_array = np.dstack([i[n_steps+n_steps_ahead-1:] for i in lag_list])\n",
    "    # We swap the last two dimensions so each slice along the first dimension\n",
    "    # is the same shape as the corresponding segment of the input time series \n",
    "    lag_array = np.swapaxes(lag_array, 1, -1)\n",
    "    return lag_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3m9tqe2KPaJt"
   },
   "source": [
    "We shall first transform the training input and output data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4kDpDwSPPaJt"
   },
   "outputs": [],
   "source": [
    "x_train = get_lagged_features(df_train[use_features], n_steps, n_steps_ahead)\n",
    "y_train =  df_train[target].values[n_steps + n_steps_ahead - 1:]\n",
    "\n",
    "x_test = get_lagged_features(df_test[use_features], n_steps, n_steps_ahead)\n",
    "y_test =  df_test[target].values[n_steps + n_steps_ahead - 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O2HykxMnPaJy"
   },
   "source": [
    "Print the shapes of each tensor. The first digit is the number of observations. For feature arrays, the second digit is the sequence length (i.e. the number of lags in the model) and the final digit is the dimension of each element in the sequence or output vector respectively. In this case, the example performs univariate time series analysis and so the dimension of the input and output is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "XyB49RUJPaJz",
    "outputId": "68ccf3ec-d641-4201-a74b-5ae87df17fdf",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(826793, 23, 1), (826793, 1), (206675, 23, 1), (206675, 1)]\n"
     ]
    }
   ],
   "source": [
    "print([tensor.shape for tensor in (x_train, y_train, x_test, y_test)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tDEkimXQPaJ5"
   },
   "source": [
    "### Model Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qhcnoaMKPaJ6"
   },
   "outputs": [],
   "source": [
    "def AlphatRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphatRNN(n_units, activation='tanh', recurrent_activation='sigmoid', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def AlphaRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphaRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))\n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def SimpleRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(SimpleRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True, stateful=False))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def GRU_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(GRU(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def LSTM_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train.shape[1], x_train.shape[-1]), unroll=True)) \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bStfen-qmDVD"
   },
   "source": [
    "Use a batch size of 1000 as the dataset is reasonably large and the training time would be too long otherwise. 2000 epochs have been used here, but a better approach would be to use a stopping criteria through a call back. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S7-KPH1NPaJ_"
   },
   "outputs": [],
   "source": [
    "max_epochs = 2000\n",
    "batch_size = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ahI8vqGYPaKF"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=50, min_delta=3e-5, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "52DPTf72PaKK"
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "    'rnn': {\n",
    "        'model': None, 'function': SimpleRNN_, 'l1_reg': 0.0, 'H': 20, \n",
    "        'color': 'blue', 'label':'RNN'}, \n",
    "    'alpharnn': {\n",
    "        'model': None, 'function': AlphaRNN_, 'l1_reg': 0.0, 'H': 10, \n",
    "        'color': 'green', 'label': '$\\\\alpha$-RNN' }, \n",
    "    'alphatrnn': {\n",
    "        'model': None, 'function': AlphatRNN_, 'l1_reg': 0.0, 'H': 5, \n",
    "        'color': 'cyan', 'label': '$\\\\alpha_t$-RNN'},\n",
    "    'gru': {\n",
    "        'model': None, 'function': GRU_,'l1_reg': 0.0, 'H': 10, \n",
    "        'color': 'orange', 'label': 'GRU'},\n",
    "    'lstm': {\n",
    "        'model': None, 'function': LSTM_, 'l1_reg': 0.0, 'H': 10, \n",
    "        'color':'red', 'label': 'LSTM'}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optionally, load pre-trained models\n",
    "Training the models takes several hours. To save time, you may load the already fitted models instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_training = False # Set to False if you wish to train the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "if do_training is False:\n",
    "    custom_objects = {'AlphaRNN': AlphaRNN, 'AlphatRNN': AlphatRNN}\n",
    "    for key in params.keys():\n",
    "        params[key]['model'] = load_model('trained-RNNs/RNNs-HFT-' + key + '.hdf5', custom_objects=custom_objects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9CdF3yGcPaKP"
   },
   "source": [
    "# Cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k-LaOtMJPaKS"
   },
   "source": [
    "The cell below performs a grid search to optimise the `n_units` and `l1_reg` for each of the models. \n",
    "\n",
    "The results are cross-validated to avoid over-fitting. Scikit-Learn's `TimeSeriesSplit` function is used to partition the data into 5 pairs of training and testing sets, where the test data is always ahead of the training data in time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XwdqakuwPaKU",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cross_val = False # WARNING: Changing this to True will take many hours to run\n",
    "\n",
    "if do_training and cross_val:\n",
    "    n_units = [5, 10, 20]\n",
    "    l1_reg = [0, 0.001, 0.01, 0.1]\n",
    "    \n",
    "    # A dictionary containing a list of values to be iterated through\n",
    "    # for each parameter of the model included in the search\n",
    "    param_grid = {'n_units': n_units, 'l1_reg': l1_reg}\n",
    "    \n",
    "    # In the kth split, TimeSeriesSplit returns first k folds\n",
    "    # as training set and the (k+1)th fold as test set.\n",
    "    tscv = TimeSeriesSplit(n_splits = 5)\n",
    "    \n",
    "    # A grid search is performed for each of the models, and the parameter set which\n",
    "    # performs best over all the cross-validation splits is saved in the `params` dictionary\n",
    "    for key in params.keys():\n",
    "        print('Performing cross-validation. Model:', key)\n",
    "        model = KerasRegressor(build_fn=params[key]['function'], epochs=max_epochs, \n",
    "                               batch_size=batch_size, verbose=2)\n",
    "        grid = GridSearchCV(estimator=model, param_grid=param_grid, \n",
    "                            cv=tscv, n_jobs=1, verbose=1)\n",
    "        grid_result = grid.fit(x_train, y_train, callbacks=[es])\n",
    "        print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "        \n",
    "        means = grid_result.cv_results_['mean_test_score']\n",
    "        stds = grid_result.cv_results_['std_test_score']\n",
    "        params_ = grid_result.cv_results_['params']\n",
    "        for mean, stdev, param_ in zip(means, stds, params_):\n",
    "            print(\"%f (%f) with %r\" % (mean, stdev, param_))\n",
    "            \n",
    "        params[key]['H'] = grid_result.best_params_['n_units']\n",
    "        params[key]['l1_reg']= grid_result.best_params_['l1_reg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JqsPBNUBPaKb"
   },
   "source": [
    "# Train models with selected parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HQ_RgC_NPaKc"
   },
   "source": [
    "If the grid search was performed, the parameters `n_units` and `l1_reg` with the best cross-validated results on the training set are used. If not, the values from the initialisation of `params` above are used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "BJ1b36zXPaKd",
    "outputId": "24bb9374-a095-43e4-8cdd-cbf18931815a"
   },
   "outputs": [],
   "source": [
    "if do_training is True:\n",
    "    for key in params.keys():\n",
    "        tf.random.set_seed(0)\n",
    "        print('Training', key, 'model')\n",
    "        model = params[key]['function'](params[key]['H'], params[key]['l1_reg'])\n",
    "        model.fit(x_train, y_train, epochs=max_epochs, verbose=2,\n",
    "                  batch_size=batch_size, callbacks=[es], shuffle=False)\n",
    "        params[key]['model'] = model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4nKo4MgOPaKh"
   },
   "source": [
    "Optionally save the fitted models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z3iMR7RCPaKi"
   },
   "outputs": [],
   "source": [
    "if do_training is True:\n",
    "    for key in params.keys():\n",
    "        params[key]['model'].save('RNNs-HFT-SAVED-' + key + '.hdf5', overwrite=True)  # creates a HDF5 file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IBNx4uMuPaKv"
   },
   "source": [
    "### Print out the value of $\\alpha \\in [0,1]$ for the alpha-RNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9hJ6qohYPaKv"
   },
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return (1 / (1 + np.exp(-x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xXjb2J5kPaK1",
    "outputId": "34a11652-e9e5-4540-9606-7301da44842c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha = 0.0632901032196725\n"
     ]
    }
   ],
   "source": [
    "model = params['alpharnn']['model']\n",
    "\n",
    "names = [weight.name for layer in model.layers for weight in layer.weights]\n",
    "\n",
    "weights = model.get_weights()\n",
    "\n",
    "for name, weight in zip(names, weights):\n",
    "    if 'alpha:0' in name:\n",
    "      print(\"alpha = \" + str(sigmoid(*weight)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-FV-w6YFPaK7"
   },
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "yJ049W-3PaK9",
    "outputId": "867a4090-bf4e-4f71-a327-5c46fd5126f7",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn_1 (SimpleRNN)     (None, 20)                440       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 461\n",
      "Trainable params: 461\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "826793/826793 [==============================] - 82s 99us/step\n",
      "206675/206675 [==============================] - 22s 108us/step\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "alpha_rnn_1 (AlphaRNN)       (None, 10)                121       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 132\n",
      "Trainable params: 132\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "826793/826793 [==============================] - 124s 150us/step\n",
      "206675/206675 [==============================] - 28s 138us/step\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "alphat_rnn_1 (AlphatRNN)     (None, 5)                 80        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 86\n",
      "Trainable params: 86\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "826793/826793 [==============================] - 135s 163us/step\n",
      "206675/206675 [==============================] - 34s 162us/step\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, 10)                360       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 371\n",
      "Trainable params: 371\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "826793/826793 [==============================] - 194s 235us/step\n",
      "206675/206675 [==============================] - 48s 232us/step\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 10)                480       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 491\n",
      "Trainable params: 491\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "826793/826793 [==============================] - 214s 259us/step\n",
      "206675/206675 [==============================] - 53s 258us/step\n"
     ]
    }
   ],
   "source": [
    "for key in params.keys():\n",
    "    model = params[key]['model']\n",
    "    model.summary()\n",
    "    \n",
    "    params[key]['pred_train'] = model.predict(x_train, verbose=1)\n",
    "    params[key]['MSE_train'] = mean_squared_error(y_train, params[key]['pred_train'])\n",
    "    \n",
    "    params[key]['pred_test'] = model.predict(x_test, verbose=1) \n",
    "    params[key]['MSE_test'] = mean_squared_error(y_test, params[key]['pred_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h8jTVCWpPaLD"
   },
   "source": [
    "We shall now apply the fitted RNN model to the training set and the test set, separately. We can then informally assess the extent\n",
    "of over-fitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OjslcI9Aqjy_"
   },
   "source": [
    "We observe the in-sample prediction is reliable (upper plots). Most of the out-of-sample prediction (lower plots) is also reliable although the end of the predicted sequence appears to degrade. Such a degradation indicates the prediction horizon might be too long and the model should be periodically retrained to incorporate more recent data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set: 826793\n",
      "testing set: 206675\n"
     ]
    }
   ],
   "source": [
    "print('training set:', len(y_train))\n",
    "print('testing set:', len(y_test))\n",
    "\n",
    "# Upper limits for indices `l` & `u` in the cells below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['rnn', 'alpharnn', 'alphatrnn', 'gru', 'lstm'])\n"
     ]
    }
   ],
   "source": [
    "print(params.keys())\n",
    "\n",
    "# Set `compare` in the cells below to a list\n",
    "# containing any subset of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "colab_type": "code",
    "id": "K_ZGaW0APaLE",
    "outputId": "13eaf4dc-1713-493e-926c-33342712abdb",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_pts = 10**4 # maximum number of  points to plot per series\n",
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling ratio for less than max_pts \n",
    "                                        # per series.  Set `None` to disable \n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = np.arange(len(y_train))[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_train'][l:u:ds]\n",
    "    label = params[key]['label'] + ' (train MSE: %.2e)' % params[key]['MSE_train']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.plot(x_vals, y_train[l:u:ds], c=\"black\", label=\"Observed\", lw=1)\n",
    "plt.xlim(x_vals.min(), x_vals.max())\n",
    "plt.xlabel('Time (ticks)', fontsize=14)\n",
    "plt.ylabel('$\\hat{Y}$', rotation=0, fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Outputs (Training)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "colab_type": "code",
    "id": "DJynBz78PaLT",
    "outputId": "c50b1e73-99b8-4ee3-b56a-5dc7a35efa94"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot - e.g. (None, 10000)\n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling to under max_pts per\n",
    "                                        # series.  Set `None` to disable \n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = np.arange(len(y_train))[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_train'][l:u:ds] - y_train[l:u:ds]\n",
    "    label = params[key]['label'] + ' (train MSE: %.2e)' % params[key]['MSE_train']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.axhline(0, linewidth=0.8)\n",
    "plt.xlim(x_vals.min(), x_vals.max())\n",
    "plt.xlabel('Time (ticks)', fontsize=14)\n",
    "plt.ylabel('$\\hat{Y}-Y$', fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Training)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 514
    },
    "colab_type": "code",
    "id": "xNG8gMwBPaLL",
    "outputId": "ea0747c1-7939-4be0-dfeb-24e3ec38cf27"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling to under max_pts per\n",
    "                                        # series.  Set `None` to disable \n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = len(y_train) + np.arange(len(y_test))[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_test'][l:u:ds]\n",
    "    label = params[key]['label'] + ' (test MSE: %.2e)' % params[key]['MSE_test']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.plot(x_vals, y_test[l:u:ds], c=\"black\", label=\"Observed\", lw=1)\n",
    "plt.xlim(x_vals.min(), x_vals.max())\n",
    "plt.xlabel('Time (ticks)', fontsize=14)\n",
    "plt.ylabel('$\\hat{Y}$', rotation=0, fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Outputs (Testing)', fontsize=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 519
    },
    "colab_type": "code",
    "id": "bGCmSgHVPaLQ",
    "outputId": "39f4f29c-590e-4a32-8c00-5663f25d567c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare = params.keys() # e.g. ['rnn', 'alpharnn'] or ['lstm']\n",
    "l, u = (None, None) # e.g. (None, 100000) lower and upper indices of range to plot \n",
    "ds = max(1, len(y_train[l:u])//max_pts) # Downsampling to under max_pts per\n",
    "                                        # series.  Set `None` to disable \n",
    "fig = plt.figure(figsize=(15,8))\n",
    "x_vals = len(y_train) + np.arange(len(y_test))[l:u:ds]\n",
    "for key in compare:\n",
    "    y_vals = params[key]['pred_test'][l:u:ds] - y_test[l:u:ds]\n",
    "    label = params[key]['label'] + ' (test MSE: %.2e)' % params[key]['MSE_test']\n",
    "    plt.plot(x_vals, y_vals, c=params[key]['color'], label=label, lw=1)\n",
    "plt.axhline(0, linewidth=0.8)\n",
    "plt.xlim(x_vals.min(), x_vals.max())\n",
    "plt.xlabel('Time (ticks)', fontsize=14)\n",
    "plt.ylabel('$\\hat{Y}-Y$', fontsize=14)\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Testing)', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "Wqrv79AZPaLZ"
   },
   "source": [
    "### Model Diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z9rlFQJMPaLb"
   },
   "source": [
    "A fitted time series model must be examined for underfitting with a white noise test. We analyze the model residuals (i.e. the error $u_t$) to determine whether it is white noise or whether it is auto-correlated. The latter case provides statistical evidence that more lags are needed in the RNN. Box and Pierce propose the Portmanteau statistic:\n",
    "\n",
    "$$Q^*(m)=T\\sum_{l=1}^m\\hat{\\tau}_l^2,$$  \n",
    "as a test statistic for the null hypothesis $H_0:\\tau_1=\\dots=\\tau_m=0$ against the alternative hypothesis $H_a:\\tau_i\\neq 0$ for some $i\\in\\{1,\\dots,m\\}$, where $T$ is the number of observations, $\\hat{\\tau}_i$ are the sample autocorrelations of the residual, and $m$ is the maximum lag used in the test. There are several heuristics in the statistics literature to determine the maximum lag such as the Schwert statistic. \n",
    "\n",
    "The Box-Pierce statistic follows an asymptotically chi-squared distribution with $m$ degrees of freedom.\n",
    "\n",
    "The Ljung-Box test statistic increases the power of the test in finite samples:\n",
    "$$Q(m)=T(T+2)\\sum_{l=1}^m\\frac{\\hat{\\tau}_l^2}{T-l}$$\n",
    "This statistic also follows an asymptotically chi-squared distribution with $m$ degrees of freedom. The decision rule is to reject $H_0$ if $Q(m)>\\chi_{\\alpha}^2$ where $\\chi_{\\alpha}^2$ denotes the $100(1-\\alpha)^{th}$ percentile of a chi-squared distribution with m degrees of freedom and is the significance level for rejecting $H_0$.\n",
    "\n",
    "The test can be time consuming and we select a subset of the residuals. Here we simply set the maximum lag to 20. In the results below, we find that the p-values are all smaller than 0.01, indicating that we can reject the null at the 99% confidence level for any lag. This is strong evidence that the model is under-fitting and more lags are needed in our model. Unlike an auto-regressive model, increasing the number of lags in the RNN does not increase the number of weights. Thus there is no danger of over-fitting by increasing the lag, although there will be an increase in the training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7bpznKjiqjzQ"
   },
   "outputs": [],
   "source": [
    "# number of samples to use for computing test statistic\n",
    "n = 100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "hS0wiuW2PaLg",
    "outputId": "68286ebe-e5d0-4865-eb78-e4903f01b391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['rnn', 'alpharnn', 'alphatrnn', 'gru', 'lstm'])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rIPFOuKAqjzV"
   },
   "outputs": [],
   "source": [
    "key = 'alpharnn'\n",
    "predicted = params[key]['pred_test']\n",
    "residual = df_test[-n:] - predicted[-n:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2-radzroqjzb"
   },
   "outputs": [],
   "source": [
    "lb, p = sm.stats.diagnostic.acorr_ljungbox(residual, lags=20, boxpierce=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WWvgE9ouqjzf"
   },
   "source": [
    "The Box-Ljung test statistics are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "Rn0gmXkDqjzg",
    "outputId": "43b35e37-8042-4dba-f2af-f2e4a611bc70"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  92331.09457   ,  177549.28282547,  255852.83614614,\n",
       "        327900.9610354 ,  394059.01818772,  454614.25222119,\n",
       "        509887.97764339,  559994.43564881,  605170.23558907,\n",
       "        645660.61353623,  686312.79112579,  727143.95394009,\n",
       "        768151.29008262,  809223.87907208,  850262.15092409,\n",
       "        891277.42521425,  932236.56495928,  973073.62128519,\n",
       "       1013803.132924  , 1054290.1890087 ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D0MmBUaDqjzk"
   },
   "source": [
    "The p-values are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "colab_type": "code",
    "id": "TTEIIp-Sqjzn",
    "outputId": "559a473f-88ec-421a-a749-6e7ca2cce3c0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "ok ML_in_Finance-RNNs-HFT.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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